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Found 4 papers, 2 papers with code
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms.
High-Dimensional Gaussian Process Inference with Derivatives
Although it is widely known that Gaussian processes can be conditioned on observations of the gradient, this functionality is of limited use due to the prohibitive computational cost of $\mathcal{O}(N^3 D^3)$ in data points $N$ and dimension $D$.
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms.
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
To alleviate this problem, several linear-time approximations, such as spectral and inducing-point methods, have been suggested and are now in wide use.
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